This paper concerns the spatial and intensity transformations that are required to adjust for the confounding effects of subject movement during functional MRI (fMRI) activation studies. An approach is presented that models, and removes, movement-related artifacts from fMRI time-series. This approach is predicated on the observation that movement-related effects are extant even after perfect realignment. Movement-related effects can be divided into those that are a function of position of the object in the frame of reference of the scanner and those that are due to movement in previous scans. This second component depends on the history of excitation experienced by spins in a small volume and consequent differences in local saturation. The spin excitation history thus will itself be a function of previous positions, suggesting an autoregression-moving average model for the effects of previous displacements on the current signal. A model is described as well as the adjustments for movement-related components that ensue. The empirical analyses suggest that (in extreme situations) over 90% of fMRI signal can be attributed to movement, and that this artifactual component can be successfully removed.

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Behavioral and neurophysiological studies suggest that skill learning can be mediated by discrete, experience-driven changes within specific neural representations subserving the performance of the trained task. We have shown that a few minutes of daily practice on a sequential finger opposition task induced large, incremental performance gains over a few weeks of training. These gains did not generalize to the contralateral hand nor to a matched sequence of identical component movements, suggesting that a lateralized representation of the learned sequence of movements evolved through practice. This interpretation was supported by functional MRI data showing that a more extensive representation of the trained sequence emerged in primary motor cortex after 3 weeks of training. The imaging data, however, also indicated important changes occurring in primary motor cortex during the initial scanning sessions, which we proposed may ref lect the setting up of a task-specific motor processing routine. Here we provide behavioral and functional MRI data on experience-dependent changes induced by a limited amount of repetitions within the first imaging session. We show that this limited training experience can be sufficient to trigger performance gains that require time to become evident. We propose that skilled motor performance is acquired in several stages: "fast" learning, an initial, withinsession improvement phase, followed by a period of consolidation of several hours duration, and then "slow" learning, consisting of delayed, incremental gains in performance emerging after continued practice. This time course may ref lect basic mechanisms of neuronal plasticity in the adult brain that subserve the acquisition and retention of many different skills.The performance of many tasks improves, throughout life, with repetition and practice. Even in adulthood simple tasks such as reaching to a target or rapidly and accurately tapping a short sequence of finger movements, which appear, when mastered, to be effortlessly performed, often require extensive training before skilled performance develops. What changes occur in the adult brain when a new skill is acquired through practice? When, and after how much practice, do these changes occur? Functional reorganization of adult mammalian sensory and motor cortical representations has been found to occur in many different animal models of brain plasticity in the last two decades, advancing the idea that throughout life the functional properties of central nervous system neurons, as well as the neural circuitry within different brain areas, are malleable and retain a functionally significant degree of plasticity (e.g., refs. 1-4). These representational changes have been shown to be induced not only in response to lesions of peripheral or central sensory input or motor output pathways but also, in normal individuals, as a result of practice and experience. The advent of new brain imaging techniques, especially functional MRI (fMRI) (5), which allows repeated mapping of cor...

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Even after realignment there is residual movementrelated variance present in fMRI time-series, causing loss of sensitivity and, potentially, also specificity. One cause is the differential deformation of the sampling matrix, by field inhomogeneities, at different object positions, i.e., a movement-by-inhomogeneity interaction. This has been addressed previously by using empirical field measurements. In the present paper we suggest a forward model of how data is affected by an inhomogeneous field at different object positions. From this model we derive a method to solve the inverse problem of estimating the field inhomogeneities and their derivatives with respect to object position, directly from the EPI data and estimated realignment parameters. The field is modeled as a linear combination of cosine basis fields, which facilitates a fast way of implementing the necessary matrix operations. Simulations suggest that the solution is tractable and that the fields are estimable given the deformed images and knowledge of the relative positions at which they have been acquired. An experiment on a subject performing voluntary movements in the scanner yielded plausible estimates of the deformation fields and their application to "unwarp" the time series significantly reduced movement-related variance.